Pathway analysis, broadly defined as a group of methods incorporating a priori biological information from public databases, has emerged as a promising approach for analyzing high-dimensional genomic data. As a member of one of seven research groups participating in the Genetic Analysis Workshop 18, , assistant professor in the department of epidemiology at the University of Alabama at Birmingham, applied pathway analysis techniques to whole-genome sequence data from the San Antonio Family Study.
[Photo: Dr. Stella Aslibekyan]
Overall, the groups found that the potential of pathway analysis to improve detection of causal variants by lowering the multiple-testing burden and incorporating biologic insights remains largely unrealized. Specifically, there is a lack of best practices at each stage of the pathway approach: annotation, analysis, interpretation, and follow-up.
Annotation of genetic variants is inconsistent across databases, incomplete, and biased toward known genes. At the analysis stage, insufficient statistical power remains a major challenge. Analyses combining rare and common variants may have an inflated type I error rate and may not improve detection of causal genes. Inclusion of known causal genes may not improve statistical power, although the fraction of explained phenotypic variance may be a more appropriate metric.
Interpretation of findings is further complicated by evidence in support of interactions between pathways as well as by the lack of consensus on how to best incorporate functional information. Finally, all presented approaches warranted follow-up studies, both to reduce the likelihood of false-positive findings and to identify specific causal variants within a given pathway. Dr. Aslibekyan and fellow researchers concluded that despite the initial promise of pathway analysis for modeling biological complexity of disease phenotypes, many methodological challenges currently remain to be addressed.
“Pathway Analysis Approaches for Rare and Common Variants: Insights from Genetic Analysis Workshop 18” was published in September in the journal Genetic Epidemiology.
Journal article: http://onlinelibrary.wiley.com/doi/10.1002/gepi.21831/abstract